Trypanosoma lewisi is an atypical trypanosome species. Transmitted by fleas, it has a high prevalence and worldwide distribution in small mammals, especially rats [1]. Although not typically thought to infect humans, there has been a number of reports of human infections by T. lewisi in Asia including a case of a fatal infection in an infant [2]. The fact that the parasite is resistant to lysis by normal human serum [3] suggests that many people, especially immunocompromised individuals, may be at risk from zoonotic infections by this pathogen, particularly in regions where there is close contact with T. lewisi-infected rat fleas. Indeed, it is also possible that cryptic T. lewisi infections exist but have hitherto gone undetected. Such asymptomatic infections have been detected for a number of parasitic infections including the related parasite T. b. gambiense [4]. 
 
Despite the fact that T. lewisi parasites pose a risk to human health, very little is known about their population structure, reproductive mode, population size or dispersal. In the article [5], Ségard et al. presented the first attempt at examining the population structure of the parasite. They developed microsatellite markers and used them to analyse a small set of samples from West Africa and Southeast Asia. Although the number of microsatellite markers is not very high and they encountered problems of PCR amplification especially of the southeast Asian samples, they did provide preliminary data that hints at a clonal population structure with rare recombination and suggests population subdivisions occurring at a scale that is equal, and probably smaller than a neighborhood of several houses with a short generation time. These are very interesting preliminary findings that will need to be validated using a larger cohort with more markers or by whole genome sequencing.
 

References

[1] Hoare CA (1972) The trypanosomes of mammals. A zoological monograph. The trypanosomes of mammals. A zoological monograph.

[2] Truc P, Büscher P, Cuny G, Gonzatti MI, Jannin J, Joshi P, Juyal P, Lun Z-R, Mattioli R, Pays E, Simarro PP, Teixeira MMG, Touratier L, Vincendeau P, Desquesnes M (2013) Atypical Human Infections by Animal Trypanosomes. PLOS Neglected Tropical Diseases, 7, e2256. https://doi.org/10.1371/journal.pntd.0002256

[3] Lun Z-R, Wen Y-Z, Uzureau P, Lecordier L, Lai D-H, Lan Y-G, Desquesnes M, Geng G-Q, Yang T-B, Zhou W-L, Jannin JG, Simarro PP, Truc P, Vincendeau P, Pays E (2015) Resistance to normal human serum reveals Trypanosoma lewisi as an underestimated human pathogen. Molecular and Biochemical Parasitology, 199, 58–61. https://doi.org/10.1016/j.molbiopara.2015.03.007

[4] Büscher P, Bart J-M, Boelaert M, Bucheton B, Cecchi G, Chitnis N, Courtin D, Figueiredo LM, Franco J-R, Grébaut P, Hasker E, Ilboudo H, Jamonneau V, Koffi M, Lejon V, MacLeod A, Masumu J, Matovu E, Mattioli R, Noyes H, Picado A, Rock KS, Rotureau B, Simo G, Thévenon S, Trindade S, Truc P, Reet NV (2018) Do Cryptic Reservoirs Threaten Gambiense-Sleeping Sickness Elimination? Trends in Parasitology, 34, 197–207. https://doi.org/10.1016/j.pt.2017.11.008

[5] Ségard A, Roméro A, Ravel S, Truc P, Gauthier D, Gauthier P, Dossou H-J, Sylvestre B, Houéménou G, Morand S, Chaisiri K, Noûs C, De Meeûs T (2022) Development of nine microsatellite loci for Trypanosoma lewisi, a potential human pathogen in Western Africa and South-East Asia, and preliminary population genetics analyses. Zenodo, 6460010, ver. 3 peer-reviewed and recommended by Peer Community in Infections. https://doi.org/10.5281/zenodo.6460010

Mosquitoes are first vector of pathogen worldwide and transmit several arbovirus, most of them leading to major outbreaks (1). Chikungunya virus (CHIKV) is a perfect example of the “explosive type” of arbovirus, as observed in La Réunion Island in 2005-2006 (2-6) and also in the outbreak of 2007 in Italy (7), both vectorized by Ae. albopictus. Being able to better understand CHIKV intra-vector dynamics is still of major interest since not all chikungunya strain are explosive ones (8). 

In this study (9), the authors have evaluated the vector competence of a local strain of Aedes albopictus (collected in Lyon, France) for CHIKV. They evaluated infection, dissemination and transmission dynamics of CHIKV using different dose of virus in individual mosquitoes from day 2 to day 20 post exposure, by titration and quantification of CHIKV RNA load in the saliva. As highlighted by both reviewers, the most innovative idea in this study was the use of three different oral doses trying to span human viraemia detected in two published studies (10-11), doses that were estimated through their model of human CHIKV viremia in the blood.  They have found that CHIKV dissemination from the Ae. albopictus midgut depends on the interaction between time post-exposure and virus dose (already highlighted by other international publications).  Then their results were implemented in the agent-based model nosoi to estimate the epidemic potential of CHIKV in a French population of Ae. albopictus, using realistic vectorial capacity parameters.

To conclude, the authors have discussed the importance of other parameters that could influence vector competence as mosquito microbiota and temperature, parameters that need also to be estimated in local mosquito population to improve the risk assessment through modelling.  

As pointed out by both reviewers, this is a nice study, well written and easy to read. These results allow filling in another gap of our understanding of CHIKV intra-vector dynamics and highlight the epidemic potential of CHIKV upon transmission by Aedes albopictus in mainland France. For all these reasons, I chose to recommend this article for Peer Community In Infections.

References

1.       Marine Viglietta, Rachel Bellone, Adrien Albert Blisnick, Anna-Bella Failloux. (2021). Vector Specificity of Arbovirus Transmission. Front Microbiol Dec 9;12:773211. https://doi.org/10.3389/fmicb.2021.773211

2.       Schuffenecker I, Iteman I, Michault A, Murri S, Frangeul L, Vaney M-C, Lavenir R, Pardigon N, Reynes J-M, Pettinelli F, Biscornet L, Diancourt L, Michel S, Duquerroy S, Guigon G, Frenkiel M-P, Bréhin A-C, Cubito N, Desprès P, Kunst F, Rey FA, Zeller H, Brisse S. (2006). Genome Microevolution of Chikungunya viruses Causing the Indian Ocean Outbreak. 2006. PLoS Medicine, 3, e263. https://doi.org/10.1371/journal.pmed.0030263

3.       Bonilauri P, Bellini R, Calzolari M, Angelini R, Venturi L, Fallacara F, Cordioli P, 687 Angelini P, Venturelli C, Merialdi G, Dottori M. (2008). Chikungunya Virus in Aedes albopictus, Italy. Emerging Infectious 689 Diseases, 14, 852–854. https://doi.org/10.3201/eid1405.071144

4.       Pagès F, Peyrefitte CN, Mve MT, Jarjaval F, Brisse S, Iteman I, Gravier P, Tolou H, Nkoghe D, Grandadam M. (2009). Aedes albopictus Mosquito: The Main Vector of the 2007 Chikungunya Outbreak in Gabon. PLoS ONE, 4, e4691. https://doi.org/10.1371/journal.pone.0004691

5.       Paupy C, Kassa FK, Caron M, Nkoghé D, Leroy EM (2012) A Chikungunya Outbreak Associated with the Vector Aedes albopictus in Remote Villages of Gabon. Vector-Borne and Zoonotic Diseases, 12, 167–169. https://doi.org/10.1089/vbz.2011.0736

6.       Mombouli J-V, Bitsindou P, Elion DOA, Grolla A, Feldmann H, Niama FR, Parra H-J, Munster VJ. (2013). Chikungunya Virus Infection, Brazzaville, Republic of Congo, 2011. Emerging Infectious Diseases, 19, 1542–1543. https://doi.org/10.3201/eid1909.130451

7.       Venturi G, Luca MD, Fortuna C, Remoli ME, Riccardo F, Severini F, Toma L, Manso MD, Benedetti E, Caporali MG, Amendola A, Fiorentini C, Liberato CD, Giammattei R, Romi R, Pezzotti P, Rezza G, Rizzo C. (2017). Detection of a chikungunya outbreak in Central Italy, August to September 2017. Eurosurveillance, 22, 17–00646. https://doi.org/10.2807/1560-7917.es.2017.22.39.17-00646

8.       de Lima Cavalcanti, T.Y.V.; Pereira, M.R.; de Paula, S.O.; Franca, R.F.d.O. (2022). A Review on Chikungunya Virus Epidemiology, Pathogenesis and Current Vaccine Development. Viruses 2022, 14, 969. https://doi.org/10.3390/v14050969

9.       Barbara Viginier, Lucie Cappuccio, Celine Garnier, Edwige Martin, Carine Maisse, Claire Valiente Moro, Guillaume Minard, Albin Fontaine, Sebastian Lequime, Maxime Ratinier, Frederick Arnaud, Vincent Raquin. (2023). Chikungunya intra-vector dynamics in Aedes albopictus from Lyon (France) upon exposure to a human viremia-like dose range reveals vector barrier permissiveness and supports local epidemic potential. medRxiv, ver.3, peer-reviewed and recommended by Peer Community In Infections. https://doi.org/10.1101/2022.11.06.22281997

10.     Appassakij H, Khuntikij P, Kemapunmanus M, Wutthanarungsan R, Silpapojakul K (2013) Viremic profiles in CHIKV-infected cases. Transfusion, 53, 2567–2574. https://doi.org/10.1111/j.1537-2995.2012.03960.x

11.     Riswari SF, Ma’roef CN, Djauhari H, Kosasih H, Perkasa A, Yudhaputri FA, Artika IM, Williams M, Ven A van der, Myint KS, Alisjahbana B, Ledermann JP, Powers AM, Jaya UA (2015) Study of viremic profile in febrile specimens of chikungunya in Bandung, Indonesia. Journal of clinical virology : the official publication of the Pan American Society for Clinical Virology, 74, 61–5. https://doi.org/10.1016/j.jcv.2015.11.017

Worldwide, ticks and tick-borne diseases are a persistent example of problems at the One Health interface between humans, wildlife, and environment (1, 2). The management and prevention of ticks and tick-borne diseases require a better understanding of host, tick and pathogen interactions and thus get a better view of the tick-borne pathosystems.

In this study (3), the tick-borne pathosystem included three component species: first a seabird host, the Yellow-legged gull (YLG - Larus michahellis, Laridae), second a soft nidicolous tick (Ornithodoros maritimus, Argasidae, syn. Alectorobius maritimus) known to infest this host and third a blood parasite (Babesia sp. YLG, Piroplasmidae). In this pathosystem, authors investigated the role of the soft tick, Ornithodoros maritimus, as a potential vector of Babesia sp. YLG. They analyzed the transmission of Babesia sp. YLG by collecting different tick life stages from YLG nests during 4 consecutive years on the islet of Carteau (Gulf of Fos, Camargue, France). Ticks were dissected and organs were analyzed separately to detect the presence of Babesia sp DNA and to evaluate different transmission pathways.

While the authors detected Babesia sp. YLG DNA in the salivary glands of nymphs, females and males, this result reveals a strong suspicion of transmission of the parasite by the soft tick. Babesia sp. YLG DNA was also found in tick ovaries, which could indicate possible transovarial transmission. Finally, the authors detected Babesia sp. YLG DNA in several male testes and in endospermatophores, and notably in a parasite-free female (uninfected ovaries and salivary glands). These last results raise the interesting possibility of sexual transmission from infected males to uninfected females.

As pointed out by both reviewers, this is a nice study, well written and easy to read. All the results are new and allow to better understand the role of the soft tick, Ornithodoros maritimus, as a potential vector of Babesia sp. YLG. They finally question about the degree to which the parasite can be maintained locally by ticks and the epidemiological consequences of infection for both O. maritimus and its avian host. For all these reasons, I chose to recommend this article for Peer Community In Infections.

References

1. Dantas-Torres et al (2012). Ticks and tick-borne diseases: a One Health perspective. Trends Parasitol. 28:437. https://doi.org/10.1016/j.pt.2012.07.003 
2. Johnson N et al (2022). One Health Approach to Tick and Tick-Borne Disease Surveillance in the United Kingdom. Int J Environ Res Public Health. 19:5833. https://doi.org/10.3390/ijerph19105833
3. Bonsergent C, Vittecoq M, Leray C, Jouglin M, Buysse M, McCoy KD, Malandrin L. A soft tick vector of Babesia sp. YLG in Yellow-legged gull (Larus michahellis) nests. bioRxiv, 2023.03.24.534071, ver. 3 peer-reviewed and recommended by Peer Community in Infections. https://doi.org/10.1101/2023.03.24.534071

Various species of Ebolavirus have caused, and are still causing, zoonotic outbreaks and public health crises in Africa. Bats have long been hypothesized to be important reservoir populations for a series of viruses such as Hendra or Marburg viruses, the severe acute respiratory syndrome coronavirus (SARS-CoV, SARS-CoV-2) as well as Ebolaviruses [2, 3]. However the ecology of disease dynamics, disease transmission, and coevolution with their natural hosts of these viruses is still poorly understood, despite their importance for predicting novel outbreaks in human or livestock populations. The evidence that bats function as sylvatic reservoirs for Ebola viruses is yet only partial. Indeed, only few serological studies demonstrated the presence of Ebolavirus antibodies in young bats [4], albeit without providing positive controls of viral detection or identifying the viral species (via PCR). There is thus an unexplained discrepancy between serological data and viral detection [2, 4]. 

In this article, Pleydell et al. [1] use a modeling approach as well as published serological and age-structure (of the bat population) data to calibrate the model simulations. The study starts with the development of an age-structured epidemiological model which includes seasonal birth pulses and waning immunity, both generating pulses of Ebolavirus transmission within a colony of African straw-coloured fruit bats (Eidolon helvum). The epidemiological dynamics of such system of ordinary differential equations can generate annual outbreaks, skipped years or multi-annual cycles up to chaotic dynamics. Therefore, the calibration of the parameters, and the definition of biologically relevant priors, are key. To this aim, the serological data are obtained from a previous study in Cameroon [5], and the age structured of the bat population (birth and mortality) from a population study in Ghana [6]. These data are integrated into the Bayesian analysis and statistical framework to fit the model and generate predictions. In a nutshell, the authors show an overlap between the data and credibility intervals generated by the calibrated model, which thus explains well the seasonality of age-structure, namely changes in pup presence, number of lactating females, or proportion of juveniles in May. The authors can estimate that 76% of adults and 39% of young bats do survive each year, and infections are expected to last one and a half weeks. The epidemiological model predicts that annual birth pulses likely generate annual disease outbreaks, so that weeks 30 to 31 of each year, are predicted to be the best period to isolate the circulating Ebolavirus in this bat population. From the model predictions, the authors estimate the probability to have missed an infectious bat among all the samples tested by PCR being approximately of one per two thousands. The disease dynamics pattern observed in the serology data, and replicated by the model, is likely driven by seasonal pulses of young susceptible bats entering the population. This seasonal birth event increases the viral transmission, resulting in the observed peak of viral prevalence. With the inclusion of immunity waning and antibody persistence, the model results illuminate therefore why previous studies have detected only few positive cases by PCR tests, in contrast to the evidence from serological data. 

 This study provides a first proof of principle that epidemiological modeling, despite its many simplifying assumptions, can be applied to wild species reservoirs of zoonotic diseases in order to optimize the design of field studies to detect viruses. Furthermore, such models can contribute to assess the probability and timing of zoonotic outbreaks in human or livestock populations. This article illustrates one of the manifold applications of mathematical theory of disease epidemiology to optimize sampling of pathogens/parasites or vaccine development and release [7, 8]. The further coupling of such models with population genetics theory and statistical inference methods (using parasite genome data) increasingly provide insights into the adaptation and evolution of parasites to human, crops and livestock populations [9, 10].

References

[1] Pleydell D.R.J., Ndong Bass I., Mba Djondzo F.A., Djomsi D.M., Kouanfack C., Peeters M., and J. Cappelle. 2023. A Bayesian analysis of birth pulse effects on the probability of detecting Ebola virus in fruit bats. bioRxiv, ver. 3 peer reviewed and recommended by Peer Community In Infections. https://doi.org/10.1101/2023.08.10.552777

[2] Caron A., Bourgarel M., Cappelle J., Liégeois F., De Nys H.M., and F. Roger. 2018. Ebola virus maintenance: if not (only) bats, what else? Viruses 10, 549. https://doi.org/10.3390/v10100549

[3] Letko M., Seifert S.N., Olival K.J., Plowright R.K., and V.J. Munster. 2020. Bat-borne virus diversity, spillover and emergence. Nature Reviews Microbiology 18, 461–471. https://doi.org/10.1038/s41579-020-0394-z

[4] Leroy E.M., Kumulungui B., Pourrut X., Rouquet P., Hassanin A., Yaba P., Délicat A., Paweska J.T., Gonzalez J.P., and R. Swanepoel. 2005. Fruit bats as reservoirs of Ebola virus. Nature 438, 575–576. https://doi.org/10.1038/438575a

[5] Djomsi D.M. et al. 2022. Dynamics of antibodies to Ebolaviruses in an Eidolon helvum bat colony in Cameroon. Viruses 14, 560. https://doi.org/10.3390/v14030560

[6] Peel A.J. et al. 2016. Bat trait, genetic and pathogen data from large-scale investigations of African fruit bats Eidolon helvum. Scientific data 3, 1–11. https://doi.org/10.1038/sdata.2016.49

[7] Nyandjo Bamen H.L., Ntaganda J.M., Tellier A. and O. Menoukeu Pamen. 2023. Impact of imperfect vaccine, vaccine trade-off and population turnover on infectious disease dynamics. Mathematics, 11(5), p.1240. https://doi.org/10.3390/math11051240

[8] Saadi N., Chi Y.L., Ghosh S., Eggo R.M., McCarthy C.V., Quaife M., Dawa J., Jit M. and A. Vassall. 2021. Models of COVID-19 vaccine prioritisation: a systematic literature search and narrative review. BMC medicine, 19, pp.1-11. https://doi.org/10.1186/s12916-021-02190-3

[9] Maerkle, H., John S., Metzger, L., STOP-HCV Consortium, Ansari, M.A., Pedergnana, V. and Tellier, A., 2023. Inference of host-pathogen interaction matrices from genome-wide polymorphism data. bioRxiv, https://doi.org/10.1101/2023.07.06.547816.

[10] Gandon S., Day T., Metcalf C.J.E. and B.T. Grenfell. 2016. Forecasting epidemiological and evolutionary dynamics of infectious diseases. Trends in ecology & evolution, 31(10), pp.776-788. https://doi.org/10.1016/j.tree.2016.07.010

Zoonotic pathogens, those transmitted from animals to humans, constitute a major public health risk with high associated global economic costs. Diseases associated with these pathogens represent more than 60% of emerging infectious diseases and predominantly originate in wildlife (1). Over the last decades, the emergence and re-emergence of zoonotic pathogens have led to an increasing number of epidemics, as illustrated by the current Covid-19 pandemic. There is ample evidence that human impact on native ecosystems such as deforestation, agricultural development, and urbanization, is linked to spillover of pathogens from animals to humans (2). However, research and calls to action have mainly focused on the importance of surveillance and prevention of zoonotic emergences along landscape interfaces, with special emphasis on tropical forests and agroecosystems, and studies and reviews pointing out the zoonotic risk associated with cities are scarce.  Additionally, cities are sometimes wrongly seen as one homogeneous ecosystem, almost exclusively human, with a Northern hemisphere-biased perception of what a city is, which fails to take into account the ecological and socio-economic diversities that can constitute an urban area.

Here, Dobigny and Morand (3) aim to draw attention to the importance of urban ecosystems in zoonotic risk and advocate that further attention should be paid to urban, peri-urban and suburban areas. In this well-organized and well-documented review, the authors show, using updated literature, that cities are places where massive contacts occur between wildlife, domestic animals, and human inhabitants (thus constituting spillover opportunities), and that it is even likely that human and wildlife contact in urban centers is more prevalent than in wild areas, perhaps contrary to intuition. Indeed, cities currently constitute the most important environment of human life and are places for millions of close interactions between humans and animals, including pets and domestic animals, wild animals through the intrusion of wild urban-adapted species (e.g., some bat, rodent, or bird species among others), manipulation and consumption of wildlife meat, and the existence of wildlife meat markets, which all constitute a major risk for zoonotic spillover. In cities, lab escapees of zoonotic pathogens also exist, and trends of adaptation to urban ecological conditions of many vectors of primary health importance is also a concern. The authors further argue that cities are predominant places for both epidemic amplification of human-human transmitted pathogens, because they are places with high human densities and population growth, and for dissemination of reservoirs, vectors and pathogens, as they are transport hubs. Dobigny & Morand further predict, likely correctly, that cities may be important places for pathogen evolution.  Finally, they propose actions and recommendations to limit the risk of zoonotic spillover events from urban ecosystems and future directions for research aiming at assessing this risk. 

The reviewers found the manuscript well-organized and presented, timely, and bringing novel contributions to the field of zoonotic emergence. I strongly recommend this article, which should benefit a large audience, particularly in the context of the current Covid-19 pandemics and the ongoing One Health initiatives aiming at preventing future zoonotic disease emergence (4). 

References

(1) Jones KE, Patel NG, Levy MA, Storeygard A, Balk D, Gittleman JL, Daszak P (2008) Global trends in emerging infectious diseases. Nature, 451, 990–993. https://doi.org/10.1038/nature06536

(2) White RJ, Razgour O (2020) Emerging zoonotic diseases originating in mammals: a systematic review of effects of anthropogenic land-use change. Mammal Review, 50, 336–352. https://doi.org/10.1111/mam.12201

(3) Dobigny G, Morand S (2022) Zoonotic emergence at the animal-environment-human interface: the forgotten urban socio-ecosystems. Zenodo, 6444776, ver. 3 peer-reviewed and recommended by Peer Community in Infections. https://doi.org/10.5281/zenodo.6444776

(4) Morand S, Lajaunie C (2021) Biodiversity and COVID-19: A report and a long road ahead to avoid another pandemic. One Earth, 4, 920–923. https://doi.org/10.1016/j.oneear.2021.06.007